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“State of AI in the Enterprise” Report, 5th Edition, Uncovers Four Key Actions to Maximize AI Value

The Deloitte AI Institute’s fifth edition of the “State of AI in the Enterprise” survey, conducted between April and May 2022, provides organizations with a roadmap to navigate lagging AI outcomes. Twenty-nine percent more respondents surveyed classify as underachievers this year, yet 79% of respondents say they’ve fully deployed three or more types of AI. It is clear despite rapid advancement in the AI market that organizations are struggling to turn implementation into scalable transformation. This year’s report digs deeper into the actions that lead to successful outcomes — providing leaders with a guide to overcome roadblocks and drive business results with AI.

Research Highlights: Pen and Paper Exercises in Machine Learning

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

Talend Data Health Barometer Reveals Companies’ Ability to Manage Data is Worsening Year-Over-Year

Talend, a global leader in data integration and data management, released the results from its second annual Data Health Barometer, a survey conducted globally among nearly 900 independent data experts and leaders. While a majority of respondents believe data is important, 97% face challenges in using data effectively and nearly half say it’s not easy to use data to drive business impact. The Data Health Barometer explores the disconnect between data and decision, which can impede enterprises and executives from supporting their strategic objectives through any economic conditions.

CIOs Say Data Management is Critical for Successful AI Adoption in New Global Research Report

A new survey report by MIT Technology Review Insights highlights AI and data management as essential pillars to enterprise success, but found that the majority of survey respondents cited data mismanagement as a critical factor that could jeopardize their company’s future AI success. The report, “CIO vision 2025: Bridging the gap between BI and AI,” was conducted in May and June 2022 in association with Databricks, pioneer of the lakehouse architecture.

AI Under the Hood: Mixing Things Up – Optimizing Fluid Mixing with Machine Learning

Fluid mixing is an important part of several industrial processes and chemical reactions. However, the process often relies on trial-and-error-based experiments instead of mathematical optimization. While turbulent mixing is effective, it cannot always be sustained and can damage the materials involved. To address this issue, researchers from Japan (Tokyo University of Science) have now proposed an optimization approach to fluid mixing for laminar flows using machine learning, which can be extended to turbulent mixing as well.

Research Highlights: Interactive continual learning for robots: a neuromorphicapproach

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

Data Fusion and Analytics for Chief Investigators: Survey Report, August 2022

Our friends over at Cognyte have a second survey on data fusion and analytics – this time for chief investigators. In their last survey, the 2022 IT leaders in the Data Fusion/Analytics Domain, the company spoke to CIOs and IT executives about their challenges and investment priorities for data fusion.

Research Highlights: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

New Report on the Data Divide

Our friends over at the Center for Data Innovation just released a new report on why the United States must address the data divide, which is the social and economic inequalities that may result from a lack of collection or use of data about individuals or communities. Gillian Diebold, the policy analyst who wrote the report, is CDI’s expert on the data divide,

Machine Learning Model Management: Ensemble Modeling 

In this contributed article, editorial consultant Jelani Harper highlights how the machine learning approach called ensemble modeling enables organizations to utilize an assortment of models and combine them, and their predictive accuracies, to get the best result.